Face detection and face verification have been conventionally performed using series operations, that is, tasks performed sequentially, in which separate algorithms are required to be trained for each task. Conventional approaches also require non-neural network techniques to perform face cropping and alignment between the tasks of face detection and face verification, thereby resulting in significant latency and using significant computing power and memory resources for the different networks.